Activation of PKA via asymmetric allosteric coupling of structurally conserved cyclic nucleotide binding domains

Cyclic nucleotide-binding (CNB) domains allosterically regulate the activity of proteins with diverse functions, but the mechanisms that enable the cyclic nucleotide-binding signal to regulate distant domains are not well understood. Here we use optical tweezers and molecular dynamics to dissect changes in folding energy landscape associated with cAMP-binding signals transduced between the two CNB domains of protein kinase A (PKA). We find that the response of the energy landscape upon cAMP binding is domain specific, resulting in unique but mutually coordinated tasks: one CNB domain initiates cAMP binding and cooperativity, whereas the other triggers inter-domain interactions that promote the active conformation. Inter-domain interactions occur in a stepwise manner, beginning in intermediate-liganded states between apo and cAMP-bound domains. Moreover, we identify a cAMP-responsive switch, the N3A motif, whose conformation and stability depend on cAMP occupancy. This switch serves as a signaling hub, amplifying cAMP-binding signals during PKA activation

A Computational Modeling and Simulation Approach to Investigate Mechanisms of Subcellular cAMP Compartmentation.

Subcellular compartmentation of the ubiquitous second messenger cAMP has been widely proposed as a mechanism to explain unique receptor-dependent functional responses. How exactly compartmentation is achieved, however, has remained a mystery for more than 40 years. In this study, we developed computational and mathematical models to represent a subcellular sarcomeric space in a cardiac myocyte with varying detail. We then used these models to predict the contributions of various mechanisms that establish subcellular cAMP microdomains. We used the models to test the hypothesis that phosphodiesterases act as functional barriers to diffusion, creating discrete cAMP signaling domains. We also used the models to predict the effect of a range of experimentally measured diffusion rates on cAMP compartmentation. Finally, we modeled the anatomical structures in a cardiac myocyte diad, to predict the effects of anatomical diffusion barriers on cAMP compartmentation. When we incorporated experimentally informed model parameters to reconstruct an in silico subcellular sarcomeric space with spatially distinct cAMP production sites linked to caveloar domains, the models predict that under realistic conditions phosphodiesterases alone were insufficient to generate significant cAMP gradients. This prediction persisted even when combined with slow cAMP diffusion. When we additionally considered the effects of anatomic barriers to diffusion that are expected in the cardiac myocyte dyadic space, cAMP compartmentation did occur, but only when diffusion was slow. Our model simulations suggest that additional mechanisms likely contribute to cAMP gradients occurring in submicroscopic domains. The difference between the physiological and pathological effects resulting from the production of cAMP may be a function of appropriate compartmentation of cAMP signaling. Therefore, understanding the contribution of factors that are responsible for coordinating the spatial and temporal distribution of cAMP at the subcellular level could be important for developing new strategies for the prevention or treatment of unfavorable responses associated with different disease states

Bridging scales through multiscale modeling: A case study on Protein Kinase A

The goal of multiscale modeling in biology is to use structurally based physico-chemical models to integrate across temporal and spatial scales of biology and thereby improve mechanistic understanding of, for example, how a single mutation can alter organism-scale phenotypes. This approach may also inform therapeutic strategies or identify candidate drug targets that might otherwise have been overlooked. However, in many cases, it remains unclear how best to synthesize information obtained from various scales and analysis approaches, such as atomistic molecular models, Markov state models (MSM), subcellular network models, and whole cell models. In this paper, we use protein kinase A (PKA) activation as a case study to explore how computational methods that model different physical scales can complement each other and integrate into an improved multiscale representation of the biological mechanisms. Using measured crystal structures, we show how molecular dynamics (MD) simulations coupled with atomic-scale MSMs can provide conformations for Brownian dynamics (BD) simulations to feed transitional states and kinetic parameters into protein-scale MSMs. We discuss how milestoning can give reaction probabilities and forward-rate constants of cAMP association events by seamlessly integrating MD and BD simulation scales. These rate constants coupled with MSMs provide a robust representation of the free energy landscape, enabling access to kinetic and thermodynamic parameters unavailable from current experimental data. These approaches have helped to illuminate the cooperative nature of PKA activation in response to distinct cAMP binding events. Collectively, this approach exemplifies a general strategy for multiscale model development that is applicable to a wide range of biological problems

A Computational Modeling and Simulation Approach to Investigate Mechanisms of Subcellular cAMP Compartmentation

<div><p>Subcellular compartmentation of the ubiquitous second messenger cAMP has been widely proposed as a mechanism to explain unique receptor-dependent functional responses. How exactly compartmentation is achieved, however, has remained a mystery for more than 40 years. In this study, we developed computational and mathematical models to represent a subcellular sarcomeric space in a cardiac myocyte with varying detail. We then used these models to predict the contributions of various mechanisms that establish subcellular cAMP microdomains. We used the models to test the hypothesis that phosphodiesterases act as functional barriers to diffusion, creating discrete cAMP signaling domains. We also used the models to predict the effect of a range of experimentally measured diffusion rates on cAMP compartmentation. Finally, we modeled the anatomical structures in a cardiac myocyte diad, to predict the effects of anatomical diffusion barriers on cAMP compartmentation. When we incorporated experimentally informed model parameters to reconstruct an in silico subcellular sarcomeric space with spatially distinct cAMP production sites linked to caveloar domains, the models predict that under realistic conditions phosphodiesterases alone were insufficient to generate significant cAMP gradients. This prediction persisted even when combined with slow cAMP diffusion. When we additionally considered the effects of anatomic barriers to diffusion that are expected in the cardiac myocyte dyadic space, cAMP compartmentation did occur, but only when diffusion was slow. Our model simulations suggest that additional mechanisms likely contribute to cAMP gradients occurring in submicroscopic domains. The difference between the physiological and pathological effects resulting from the production of cAMP may be a function of appropriate compartmentation of cAMP signaling. Therefore, understanding the contribution of factors that are responsible for coordinating the spatial and temporal distribution of cAMP at the subcellular level could be important for developing new strategies for the prevention or treatment of unfavorable responses associated with different disease states.</p></div

Stochastic simulation of cAMP diffusion implemented in MCell and visualized using CellBlender.

<p>The diffusion coefficient was set to <b><i>10 μm</i></b>^{<b><i>2</i></b>}<b><i>/s</i></b>. (A) 10 PDE molecules (~4.1514 μM). Four snapshots are shown in the left, the average cAMP concentration for the 1800 time frames between 1s to 10s are shown shown in the blue bar graph, and the time course of the spatially averaged cAMP concentration is shown in the top right panel. (B) PDE molecules = 100 (~41.514 μM). Average of cAMP molecules for 1800 time frames from 1s to 10s at steady state. (C) PDE molecules = 1000 (~415.14 μM). Average of cAMP molecules over 1800 time frames from 1s to 10s at steady state. (D) PDE molecules = 10000 (~4151.4 μM). Average of cAMP molecules for 1800 time frames from 1s to 10s at steady state. (E) PDE molecules = 100000 (~41514 μM). Average of cAMP molecules over 1800 time frames from 1s to 10s at steady state. The red curves plotted on the accumulated concentration maps in panel (B-E) show the predictions of the 1D continuum model. In all cases, there is excellent agreement with the full 3D stochastic model. The cAMP compartmentation ratio <i>R</i> for the various values of PDE concentration shown in panels (A-E) are 1.826 x 10⁻³, 8.381 x 10⁻², 4.970 x 10⁻¹, 9.087 x 10⁻¹, and 9.901 x 10⁻¹,</p

Initial values.

<p>Initial values.</p

Idealized partialDE model demonstrating cAMP generation and diffusion (diffusion coefficient 300 μm²/s) from t-tubular caveolar microdomains (seen as rectangles along edge of inset) at various time points following β₁AR stimulation with 30 nM isoproterenol (basal cAMP = 0.1 μM).

<p>(A) As expected, in the absence of PDEs or anatomical barriers, diffusion is rapid with miniscule gradients and cAMP concentration grows unboundedly. (B) To simulate effects of PDEs, experimentally measured concentrations of PDEs were added into caveolar microdomains (10 PDE molecules/cavelor domain), and (C) Effect of 10-fold increase in the concentration of PDEs. No gradients were observed. (D) When diffusion coefficient was set to 60 μm²/s, the simulated results showed sub-nanomolar gradients before 1.0 second. (E) Diffusion coefficient was set to 10 μm²/s. Miniscule gradients (sub-nanomolar) were observed during the early time periods (before 1.0 second) when concentration of PDE was increased 10 fold.</p

Model parameters from [61, 62].

<p>Model parameters from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005005#pcbi.1005005.ref061" target="_blank">61</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005005#pcbi.1005005.ref062" target="_blank">62</a>].</p